(1) Field of the Invention
The present invention relates to an image processing technique of detecting a moving object by specifying a region in the moving object in an image. Particularly, the present invention relates to a moving object detection apparatus or the like which detects a moving object at high speed based on motion information in video even when the moving object to be detected moves changing shape like a person.
(2) Description of the Related Art
Conventionally, research and development has been widely promoted on, a region extraction method for detecting a moving object by extracting, from an image including a moving object (hereinafter, simply referred to as a “moving object”), a region of the moving object within the image. Particularly, in the case of a human moving object, extracting a moving object region is a basic technique commonly used for: focus control image improvement processing in a digital camcorder or a digital still camera; a driving safety support system for vehicles; or collision avoidance control to avoid collision against a person or alarming using a robot.
Among techniques for extracting a region of the moving object in the image, a method generally used is extracting a candidate moving object region from the image, then evaluating similarity of the extracted moving object region candidate with respect to a moving object model that is prepared in advance, and extracting a region having high similarity to a moving object region.
Furthermore, for extracting a moving object which walks changing shape, such as a walking person or the like, another method using a moving object model considering shape change is used. For example, in the technique disclosed in Patent Reference 1, a silhouette image of the moving object is extracted as a moving region candidate from each of a plurality of images. Then, Patent Reference 1 discloses a technique of evaluating similarity between a model related to the shape change of the previously parameterized moving object and the extracted silhouette image, and estimating parameters of a region having high similarity and the model corresponding to the region. Since this allows applying the parameterized model to a human figure that moves periodically changing shape, it is possible to perform extraction of the moving object region.
In addition, in the technique disclosed in Non-Patent Reference 1, with input of images each including a fixed moving object captured from a plurality of viewpoints, a Euclidean distance between a vector made up of arrangements of pixel values in each image and a vector made up of arrangements of pixel values in another image is calculated. Then, Non-Patent Reference 1 discloses a technique of transforming the calculated Euclidean distance into a geodetic distance, and then performing dimensional reduction so that images captured from similar viewpoints are projected close to each other on a two-dimensional space. Here, it is shown that compared to a conventional linear dimensional reduction method such as Principal Component Analysis (PCA), the technique allows performing lower-dimensional reduction through geodetic distance transformation and further allows processing the data that is nonlinearly distributed.
Patent Reference 1: Japanese Unexamined Patent Application Publication No. 8-214289
Non-Patent Reference 1: Joshua Tenenbaum, Vin de Silva and John Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction”, Science, Vol. 290, pp. 2319-2322, 22 December, 2000.